[1]沈怡灵,赵明哲,李强懿,等.基于稀疏表示的二值图像超分辨率重建算法[J].计算机技术与发展,2017,27(12):43-47.[doi:10.3969/ j. issn.1673-629X.2017.12.010]
　SHEN Yi-ling,ZHAO Ming-zhe,LI Qiang-yi,et al.A Super-resolution Reconstruction Algorithm for Binary Image Based on Sparse Representation[J].Computer Technology and Development,2017,27(12):43-47.[doi:10.3969/ j. issn.1673-629X.2017.12.010]
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基于稀疏表示的二值图像超分辨率重建算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年12期
页码:
43-47
栏目:
出版日期:
2017-12-10

文章信息/Info

Title:
A Super-resolution Reconstruction Algorithm for Binary Image Based on Sparse Representation
文章编号:
1673-629X(2017)12-0043-05
作者:
沈怡灵 1 ; 赵明哲 1 ; 李强懿 1 ; 李博涵 1 2 3
1. 南京航空航天大学 计算机科学与技术学院,江苏 南京 210016;
2. 软件新技术与产业化协同创新中心,江苏 南京 210093;
3. 江苏易图地理信息科技股份有限公司,江苏 扬州 225009
Author(s):
SHEN Yi-ling 1 ; ZHAO Ming-zhe 1 ; LI Qiang-yi 1 ; LI Bo-han 1 2 3
1. School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;
2. Collaborative Innovation Center of Novel Software Technology and Industrialization,Nanjing 210093,China;
3. Jiangsu E-Map Geographic Information Technology Co. ,Ltd,Yangzhou 225009,China
关键词:
二值图像; 稀疏表示; 超分辨率重建; 特征提取; 字典学习
Keywords:
binary image; sparse representation; super-resolution reconstruction; feature extraction; dictionary learning
分类号:
TP301.6
DOI:
10.3969/ j. issn.1673-629X.2017.12.010
文献标志码:
A
摘要:
目前,关于二值图像的超分辨率重建的研究很少,而二值图像应用广泛,低分辨率的二值图像会导致对其识别困难。 针对这一现状,对基于稀疏表示的二值图像的超分辨率重建进行深入研究,提出了一种针对二值图像的超分辨率重建算法。 一方面,分析二值图像具有的明显特征,对其进行针对性的边缘特征及纹理特征的提取,以更精确地表示二值图像的高频信息,提供更多的先验信息,提高二值图像的重建质量。 另一方面,针对二值图像中存在二维码图像、文本图像等不同类型的图像这一特点,将聚类算法融合到字典学习中,使得学习得来的字典更适用于不同类型的二值图像。 实验结果表明,提出的针对二值图像的基于稀疏表示的超分辨率重建算法对二值图像有很好的重建效果,对噪声具有一定的鲁棒性。
Abstract:
At present,there is little research on binary image super-resolution reconstruction. Binary image is widely used,but that of lowresolution will result in identification difficulties. In view of this,with deep research on binary image super-resolution reconstruction based on sparse representation,a super-resolution reconstruction algorithm for binary image is proposed. On the one hand,obvious features of the binary image are analyzed,and the edge and texture features of that are extracted to represent its high frequency information more accurately. Therefore,more priori information is provided,and the reconstructed quality of binary image is improved. On the other hand,for the different types in binary images,such as two-dimensional bar code and text,the clustering algorithm is integrated into dictionary learning,so that the learned dictionaries are more suitable for different types of binary image. Experimental results show that the proposed algorithm has a good effect in reconstruction for the binary image,with a certain robustness against noise.

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更新日期/Last Update: 2018-03-05